Submission¶

Put the ipynb file and html file in the github branch you created in the last assignment and submit the link to the commit in brightspace

In [252]:
from plotly.offline import init_notebook_mode
import plotly.io as pio
import plotly.express as px

init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"
In [253]:
#load data
df = px.data.gapminder()
df.head()
Out[253]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4

Question 1:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [254]:
# YOUR CODE HERE
pop_conti = df[df['year'] == 2007].groupby(['continent'])['pop'].sum()
fig = px.bar(pop_conti, x = pop_conti.values, y = pop_conti.index, color = pop_conti.index)
fig.update_layout(xaxis_title = 'pop')
fig.show()

Question 2:¶

Sort the order of the continent for the visualisation

Hint: Use axis layout setting

In [255]:
# YOUR CODE HERE
# update
fig.update_yaxes(categoryorder = "max ascending")
fig.show()

Question 3:¶

Add text to each bar that represents the population

In [256]:
# # YOUR CODE HERE 
# See https://plotly.com/python/text-and-annotations/#multiple-annotations, chapter "Text Case"
pop_abb = list(map(lambda x: f"{x/1e9:.1f}B" if x >= 1e9 else f"{x/1e6:.0f}M", list(pop_conti.values)))
fig = px.bar(pop_conti, x = pop_conti.values, y = pop_conti.index, color = pop_conti.index, 
             text = pop_abb)
fig.update_yaxes(categoryorder = "max ascending")

Question 4:¶

Thus far we looked at data from one year (2007). Lets create an animation to see the population growth of the continents through the years

In [243]:
# YOUR CODE HERE
pop_conti = df.groupby(['continent','year'])['pop'].sum()
pop = list(pop_conti.values)
conti = list(pop_conti.index.get_level_values(0)) # Indexing the first index in a MultiIndex object by .get_level_values(0)
year = list(pop_conti.index.get_level_values(1))
fig = px.bar(pop_conti, x = pop, y = conti, color = conti, 
             text = list(map(lambda x: f"{x/1e9:.1f}B" if x >= 1e9 else f"{x/1e6:.0f}M", pop)),
             animation_frame = year,
             range_x = [0, 4e9])
# text_auto = True show 3.8G, which is d3 default. I dont wish to override d3 format.
fig.update_layout(xaxis_title = 'pop', yaxis_title = 'continent')
fig.update_yaxes(categoryorder = "max ascending")
fig.show()

Question 5:¶

Instead of the continents, lets look at individual countries. Create an animation that shows the population growth of the countries through the years

In [244]:
# YOUR CODE HERE
pop_conti = df.groupby(['country','year'])['pop'].sum()
pop = list(pop_conti.values)
conti = list(pop_conti.index.get_level_values(0)) # Indexing the first index in a MultiIndex object by .get_level_values(0)
year = list(pop_conti.index.get_level_values(1))
fig = px.bar(pop_conti, x = pop, y = conti, color = conti, 
             animation_frame = year,
             range_x = [0, 1.5e9])

fig.update_layout(xaxis_title = 'pop', yaxis_title = 'country',
                  showlegend=False)
fig.update_yaxes(categoryorder = "max ascending")
fig.show()

Question 6:¶

Clean up the country animation. Set the height size of the figure to 1000 to have a better view of the animation

In [245]:
# YOUR CODE HERE
fig.update_layout(height = 1000)

Question 7:¶

Show only the top 10 countries in the animation

Hint: Use the axis limit to set this.

In [246]:
# YOUR CODE HERE
fig.update_yaxes(range=(131.5, 141.5), automargin = True) # try...
In [247]:
fig.update_yaxes(minallowed = 132)